Artificial Neural Networks (NN) are already heavily involved in methods and applications for
frequent tasks in the field of computational chemistry such as representation of potential …

Development and applications of neural network (NN)‐based approaches for representing
potential energy surfaces (PES) of bound and reactive molecular systems are reviewed …

This monograph presents recent advances in neural network (NN) approaches and
applications to chemical reaction dynamics. Topics covered include:(i) the development of …

On the basis of a new extensive database constructed for the purpose, we assess various
Machine Learning (ML) algorithms to predict energies in the framework of potential energy …

We review progress in neural network (NN)-based methods for the construction of
interatomic potentials from discrete samples (such as ab initio energies) for applications in …

The accuracy of the results obtained in molecular dynamics or Monte Carlo simulations
crucially depends on a reliable description of the atomic interactions. A large variety of …

We introduce a free and open-source software package (PES-Learn) which largely
automates the process of producing high-quality machine learning models of molecular …

Despite the ever-increasing computer power, accurate ab initio calculations for large
systems (thousands to millions of atoms) remain infeasible. Instead, approximate empirical …

Abstract Machine learning techniques have been widely applied in many fields of chemistry,
physics, biology, and materials science. One of the most fruitful applications is machine …

The potential energy of molecular species and their conformers can be computed with a
wide range of computational chemistry methods, from molecular mechanics to ab initio …